How AI May Beat Quantum Computing at Its Own Game

AI Has Shown It Can Imperfectly But Acceptably Do Much That Was Envisioned for Quantum Computers

- SIDDHARTH PAI

For decades, quantum computing has been described as the 21st century's technological lodestar—with its unfathomable computational power poised to solve problems beyond the ken of classical machines. Quantum computers promise to crack cryptographic codes, simulate the quantum dynamics of molecules in material science, aid drug discovery, and more. Yet, as the quantum race drags on, an unexpected challenger has emerged, not to dethrone but outpace it in precisely those domains where it was expected to shine the brightest: AI.

To grasp the possibility of this disruption, begin with what quantum computing is. Unlike classical computers that encode information in binary bits—0s or 1s—quantum computers use quantum bits, or qubits, which can exist in a superposition of states. Through entanglement and quantum interference, quantum computers can process a vast space of possibilities in parallel. This lets them model quantum systems naturally, making them ideal for simulating molecules, designing new materials, and solving certain optimization problems. Among its most touted applications is its potential to transform material science. Advances with high-temperature superconductors, catalytic surfaces, or novel semiconductors often require modeling the interactions of strongly correlated electrons—systems where the behavior of one particle is tightly linked to that of many others. Classical algorithms falter in such simulations because the complexity of the quantum state space rises exponentially with system size. A full-fledged quantum computer would handle all this with ease.